Orthogonal time frequency space (OTFS) modulation is a two-dimensional modulation scheme designed in the delay-Doppler (DD) domain, exhibiting superior performance over orthogonal frequency division multiplexing (OFDM) modulation in environments with high Doppler frequency shifts. We investigated the channel estimation in the DD domain of OTFS systems, modeling it as a sparse signal recovery problem. Subsequently, within the existing sparse Bayesian learning framework, we proposed an adaptive Bayesian threshold-based active denoising mechanism. Combined with inverse-free sparse Bayesian learning, this effectively addresses the pseudo-peak issue in low signal-to-noise ratio (SNR) scenarios while maintaining low complexity. The simulation results demonstrate that this algorithm outperforms existing channel estimation algorithms in terms of anti-noise performance and complexity.

翻译：正交时频空间（OTFS）调制是一种在时延-多普勒（DD）域中设计的二维调制方案，在高多普勒频移环境中表现出优于正交频分复用（OFDM）调制的性能。我们研究了OTFS系统在DD域中的信道估计问题，将其建模为稀疏信号恢复问题。随后，在现有稀疏贝叶斯学习框架内，我们提出了一种基于自适应贝叶斯阈值的主动去噪机制。该方法结合无逆稀疏贝叶斯学习，在保持低复杂度的同时，有效解决了低信噪比（SNR）场景下的伪峰值问题。仿真结果表明，该算法在抗噪声性能和复杂度方面均优于现有信道估计算法。